Translating Biomarkers From the Laboratory to the Clinic




Mads Røpke, Senior Scientist at LEO Pharma, joins Pharma IQ to discuss the global companion diagnostics market and how we can ensure the delivery of safer and more efficacious drugs to market. To listen to the podcast now go to Delivering the Best Drugs to Market – With Companion Diagnostics.

Pharma IQ: Can you outline briefly how companion diagnostics can help us to administer the right drugs at the right time, in the right dose, to the right patient?

M Røpke: If we could discriminate between different diseases in a diagnostic-type product the way we should do, we would identify first of all the proper disease, so we don’t muddy the water by including patients with other diseases; that’s the important step. In practice, this is often done by the doctor, but in some cases more sophisticated diagnostic tools are necessary. So that's important and something we are working on at the moment. The next question is the dose that you administer and the time, and so on. Obviously that is something for most of the compounds as such, and knowledge about how the particular compounds act in different patients at a different dose. So, a good correlation between the clinical effect of the pharmaco dynamics and the pharmaco kinetics of the compound and the doses necessary. A lot of work goes into that kind of correlation; so linking pharmaco dynamics and the pharmaco kinetics from the pre-clinical phase and into the clinic; getting a good model established that could help identifying the appropriate dose for clinical studies and also for the patients in the market.

It is a challenge and something a lot of people are working on, especially in determining within a core of patients with the same clinical diagnosis which patients are more likely to benefit from the treatment; that's really the question, and I think that's the challenge we have and it becomes more and more interesting as we move into kinetic post-blockbuster era where it’s acknowledged that we should tailor-make the treatments more to individual patients. That's really where the challenge is; I think where also the companion diagnostics has the most important place. And I think we don’t have a lot of experience in this particular area, but would like to explore our current compounds and our future compounds in this aspect. So that's what I also want to hopefully get something back on in the conference in February.      

Pharma IQ: Just to expand on this, in this developing area, companion diagnostics can sometimes prove costly and difficult to implement and utilise effectively. So, to expand upon your earlier points, what are the main problems and challenges?

M Røpke: The main problems is that... if the question is which patients within the same diagnosis have the best benefit, then obviously the validation data is so big, so what data do you have to support that some patients will react or respond differently to others? That's number one. So do you see a heterogeneity in your response and you probably do, and then you need to look for - in those particular patients - what markers would discriminate them. What could you use to discriminate these patients? And you need, first of all, to have an understanding that, in fact, there is the heterogeneity and then you get samples, appropriate samples, from the patients in the different co-ords. Often this is done in the different tiers. So first you will, in Phase II maybe, identify the variability, if you have enough patients. And then, in a subsequent study, or maybe even in the same study, you have samples from those different patients, and then you study them. And then typically, you might have an idea for a marker or set of markers, but sometimes you won't, and then you will need a more holistic or a mixed driven approach, I would say, to identify markers that could discriminate, and eliminate them.

But there are very few examples of this being the case, other than the very targeted treatments such as Herceptin and Gliem, and so on.

So I think the problems or the main problems and challenges are to get the right samples to validate the whole concept that this is playing a role, and then to identify the markers necessary. Obviously then you need to transform that into a diagnostic tool that you can use and implement that; that's more of a technical nature. I think that's do-able, but the whole validation step is quite important; it’s something that requires a lot of work and a lot of investment from companies, and also requires a lot of patience.

Pharma IQ:Absolutely. And then breaking it down, what are the vital stages in translating biomarkers from the laboratory to the clinic? And indeed, what are the best ways of successfully validating them?

M Røpke: As I said, I think it could either be very obvious what markers could be selectors. So if you know or have a good handle on the pathway that is interfered with by your compound then it’s natural to look for either, by protein analysis or transcriptomics or RNA, for example, in samples of biopsies to look for that, for members of that particular pathway that would be natural. And then you translate that and if that works and you see regulation in the preclinical models being cell-based assays, animal data, animal models, and so on, then you can transfer that in and look for the same markers in the clinic. We also very often use patient materials simulated in vitro or ex vivo or healthy donors, broad samples, for example, simulated for that particular compound to find target markers that could be used.

But I think the way that… in the other case, if you don’t have a particular view or you’ve proven that the markers you most obviously would choose were not regulated or could not be used, then you need to look at another approach and then I would say you should use a more broad or a mixed-array approach to have an unbiased view and look for basically everything that moves in those patient samples or on those animals. It’s obviously the risk of having a false positive that needs to be challenged and supported by an increasing number of samples and also increasing the quality of the methods, or the array will probably take you only so far then you need to validate that with a qPCR method or something else. But that will be my process.

So either you have a good idea and a good hypothesis that proves right or that is disproven; or you can’t get it to work and then you need a mixed or more broad array approach.

And then of course, when you get something that looks promising then again that should be converted into something that is useful in the clinic that doesn’t take a lot of time to test, to analyse, and you can implement it in the patient setting. That's obviously a whole other story and also a challenge, but something that we have some experience in and a lot of groups and companies are working on.

So that's more back to clinical pharmacology and what is possible in the clinic and what type of method. Obviously, if you have a very elaborate cell separation system and need that before you can make the measurements, it’s difficult to do in a standard clinical practice. You need something very simple. So putting all that together you get a good idea of a plan for developing your marker.

Pharma IQ:Yes. Thanks Mads, some useful tips there. Thanks for breaking that down. And now to look at the regulatory picture: how do requirements of regulatory bodies around the world, especially Europe versus the US, differ?

M Røpke: I don’t have a lot of comments on that, because the only interactions at this point we have with regulators is on more on the ethics around sampling material from the clinic. We don’t have experience in submitting packages that support using a companion diagnostic in the clinic or to steer any treatments. We're not that far down the road so we can share any experience on that. So we're more on the ethics and I guess that's pretty much the same. But we mostly operate in Europe with this. So I couldn’t be very specific on that point.

Pharma IQ: Sure, no problem. And can you report on any innovative ways coming on-stream to use companion diagnostics to advance drug development and improve efficacy?

M Røpke: I would say two points: one is sampling – so how little sample did you get away with. Could you imagine that a typing based on, maybe a blood sample or not even that, maybe a hair sample or whatever… we operate in terms of diseases; so one problem we have is having to take skin biopsies, punch biopsies all the time and if we can get away with less material, maybe a safe strip or something else of the skin, that could help. On the other side, the analytical side, there're a lot of new methods that only require very limited amount of material to make a good analysis, for example, of RNA; you don’t need a lot of cells. So I think can’t be very specific but I would say, without being too specific on the work we’re doing at the moment, I think those are probably the best. So basically you’re going down a sample you need, improving your sensitivity by new methods. I can’t be more specific than that; I'm sorry.

Pharma IQ: No problem. Then finally, drawing on your experience, can you offer any advice on finding, managing, and extracting the most value out of diagnostic partnerships?

M Røpke: We have, in Denmark, an established collaboration between academia and industry and I guess a lot of countries have that. So we think that's useful in this context because the academic institutions often have access to a lot of samples from hospitals, for example; hospital groups and so on have a good understanding of the basic science as a basic pathway.

So those two items make them interesting for us to collaborate with. So typically we have a setup where we have academia and clinical input from hospitals and the university. And then our input is more on obviously having compounds that have particular pharmacological patterns and also have samples from clinical studies and animal models; a lot of animal model data that could tie it all together. We have obviously a commercial interest in having a diagnostics... So that's the way it goes with some academic groups that are selected because they can contribute with scientific information and knowledge and have more an interest in science and publishing data. And we have an interest in the commercial side and obviously there's is a challenge that we have: we need time to patent findings and so on. And that is in the interest of the academic partner side, mostly on publishing.

So we need to balance that but that is a well-known conundrum, I think this is handled pretty well in our collaborations.

So those three elements: the clinical input from doctors, the academic input from investigations and basic research; and then the commercial input with a lot of new compounds and lots of screening data and animal data, pharmacological data that's proved to be important elements for all three in collaborations. So extracting most value is basically keeping in close interaction and close, effective meetings. And also acknowledging the different approaches and the different interests of the partners; I think that works quite well. So we know what really is interesting for each of us, and then try to accommodate that in the collaboration.

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